The spinning speed parameter plays a crucial role in determining the properties of an n-TiO₂/ZnO bilayer thin film fabricated using the sol–gel spin-coating technique, especially for solar cell applications. In this study, various spinning speeds were employed on an n-TiO₂/ZnO bilayer thin film, and characterizations were conducted, such as morphological, structural, and optical properties. The findings revealed that the optimal conditions for the thin film were achieved at a spinning speed of 3000 rpm. Under this condition, a homogenous and compact surface morphology was observed, with an even distribution of ZnO grains. The successful fabrication of an n-TiO₂/ZnO bilayer thin film was confirmed by the presence of characteristic peaks for both TiO₂ and ZnO. Obviously, three dominant ZnO orientation peaks, which included (100), (002), and (101) were identified. The prevalence of the (002)-ZnO orientation plane indicated a high-quality structure with excellent crystallinity. In terms of optical properties, the achievement of high transmittance up to 75% resembles the high transparency of the thin film. The optical energy of the n-TiO₂/ZnO bilayer thin film is estimated at 3.10 eV. In summary, the spinning speed parameter played a pivotal role in enhancing various properties of the thin film, making it a significant factor in its development for diverse applications.

In this study, titanium dioxide (TiO₂) was deposited onto a fluorine-doped tin oxide (FTO) substrate using the sol–gel spin coating method. Through the implementation of calcination treatment on the thin film, enhancements were observed in terms of structural, optical, and morphological properties. Various calcination temperatures were explored, with TiO₂ annealed at 600 °C identified as the optimal sample. Analysis of the X-ray diffraction spectroscopy (XRD) pattern revealed the prominent orientation plane of (101), indicating the presence of anatase TiO₂ with a tetragonal pattern at this temperature. Despite fluctuations in the optical spectrum, the highest transmittance of 80% was observed in the visible region within the wavelength range of 400 nm. The estimated band-gap value of 3.45 eV reaffirmed the characteristic of TiO₂. Surface analysis indicated the homogeneous growth of TiO₂, uniformly covering the FTO substrate. Cross-sectional examination revealed a thickness of 263 nm with dense and compact nature of TiO₂ thin film. No presence of defects or pores reflects a well-organized structure and high-quality formation. Significant electrical rectification properties were observed, indicating the successful formation of a p–n junction. In summary, calcination treatment was found to be crucial for enhancing the properties of the thin film, highlighting its significance in the development of solar cell applications.

Granulation is an important class of production processes in food, chemical and pharmaceutical manufacturing industries. In urea fertilizer manufacturing, fluidized beds are often used for the granulation system. However, the granulation processes release ammonia to the environment. Ammonia gas can contribute to eutrophication, which is an oversupply of nitrogen and acidification to the ecosystems. Eutrophication may cause major disruptions of aquatic ecosystems. It is estimated that global ammonia emissions from urea fertilizer processes are approximately at 10 to 12 Tg N/year, which represents 23% of overall ammonia released globally. Therefore, accurate modeling of the ammonia emission by the urea fertilizer fluidized bed granulation system is important. It allows for the system to be operated efficiently and within sustainable condition. This research attempts to optimize the model of the system using the particle swarm optimization (PSO) algorithm. The model takes pressure (Mpa), binder feed rate (rpm) and inlet temperature (°C) as the manipulated variables. The PSO searches for the model’s optimal coefficients. The accuracy of the model is measured using mean square error (MSE) between the model’s simulated value and the actual data of ammonia released which is collected from an experiment. The proposed method reduces the MSE to 0.09727, indicating that the model can accurately simulate the actual system.

A binder additional process in urea granule fertilizer by using adaptive fuzzy logic control NORHIDAYAH Mohamad, NOR AZLINA Ab Aziz, NOR HIDAYATI Abdul Aziz

Predicting lift and drag in hydro turbine design is important to optimize its performance. However, it poses significant challenges due to the complexity of fluid dynamics, which is traditionally addressed by Reynolds-Averaged Navier–Stokes equations, which is time-consuming. Moreover, these methods are computationally demanding, making them a costly approach and less efficient for complex turbine designs. Recent advancements in machine learning (ML) offer a promising alternative with reduced computational costs while maintaining accuracy. This paper explores the use of a data-driven ML model for predicting aerodynamic performance, specifically lift and drag, in hydro turbine design. The models were developed from experimental hydro turbine data gathered from various blade designs and flow conditions. CatBoost yielded the highest predictive accuracy among all the models tested. The findings indicate that CatBoost achieved the best predicted accuracy, followed by LGBM, demonstrating the efficacy of machine learning methodologies in modeling hydrodynamic forces in turbine design.

Urea fertilizers are widely used in agriculture as they are an affordable and effective source of nitrogen for plant growth. However, they can also have negativ Urea fertilizers are widely used in agriculture as they are an affordable and effective source of nitrogen for plant growth. However, they can also have negativ